A point cloud is a collection of three-dimensional points. Viewing point clouds is preferably done in a pseudo-three dimensional format displayed on a computer screen. It is further preferable to view point clouds in an interactive manner so the user can change the orientation of the data relative to the user's point of view in real time. However, point clouds with large numbers of elements strain the capabilities of computers to represent the elements of a point cloud in a responsive manner. When hardware capabilities have been reached, the only option is to remove points from the data cloud for display. The goal is to remove enough points to achieve acceptable performance while retaining enough points to provide visual detail to the user.
Imaging sensors such as laser radar sensors (LADARs) and light detection and ranging sensors (LIDARs) acquire point clouds of a scene. These data sets can be quite large, requiring significant amounts of computing resources to process. Thus, attempts have been made to thin these point clouds to reduce the set of data points that need to be worked with for a given task.
One prior method for thinning a point cloud involves removing points by a pattern (i.e. every nth point) or randomly. FIG. 1 depicts such a method. Under this approach, random points, or scattered points, are removed from the point cloud. This approach may omit important detail and may also introduce unwanted artifacts into the display.
Another prior method for thinning a point cloud involves segmenting the display into distinct tiles. Each tile then represents groups of data points at various levels of detail. Eyeglass (MIT Lincoln Laboratories) is an application for viewing point clouds using such a tiled approach. See generally “Real-Time 3D Ladar Imaging,” Lincoln Laboratory Journal, Vol. 16, Number 1, 2006, the disclosure of which is hereby incorporated by reference herein. An example of a point cloud thinned using the tile method is depicted in FIG. 2. Tiles 10a, 10b, and 10d, which appear closer to the user's perspective 20 may be thinned less than (and thus include more detail) than tiles placed further away (i.e. 10c, 10e, and 10f). This approach has the disadvantage of possibly omitting detailed data near the user. Additionally, the tile boundaries appear in the display.
Hence, there exists a need in the industry to overcome these problems and provide a method and system for thinning a point cloud. Additionally, there exists a need in the industry to thin a point cloud based upon the distance of the respective points in the point cloud from a point of view of the imaging device used to generate the point cloud.